1 Introduction:

Directorate of Registration and Stamp Revenue (hereinafter, DoRSR), West Bengal supervises the affair of registration of documents in the state as well as the collection of revenue through Stamps and Registration thereof. Currently, it contributes about 9.5% of the State’s own tax revenue (SOTR).1

At present all the registrations in the state are made through ‘e-Nathikaran’ - which is a web-based system being run by a central server. Base values of all the properties of the State are stored in 2 ‘e-Nathikaran’ which feeds both the Registration offices as well as the website ‘wbregistration.gov.in’ for market value generation.3

Currently, annual, monthly, and fortnightly revenue data of the districts are collected and assembled by DIGR(Revenue) at the Headquarters 4- and this casts a birds’ eye view on the trends. However, the huge amount of data placed at our disposal by ‘e-Nathikaran’ opens the possibility of exploring these in a structured and systematic manner - which can be conducted centrally at the Headquarter level by the officers of DoRSR - using modern analytical tools. 5 This will help in

Also there is ample scope of utilising and analysing various data like ROR, Census data etc which are either available in public domain or can be made available from government departments 6alongside, and of exploring whether they exert any weightage

and thereby aiding the ‘Scientific Framework of Market value’7(hereinafter, Scientific Framework) intiative taken up by DoRSR.

2 Approach:

For effective use of analytics at the DoRSR the steps specified in Figure. 2.1 can be followed.At first all the registration offices of the state can be categorised into diffent types on the annual revenue as well as on the number of documents they register annually.(Sec 2.1).

Then a regular analysis of revenue data of the offices is to be taken up.(Sec 2.2).

This step is actually an expansion of the existing works being done by the DIGR(Revenue) towards much deeper and structured end.

Besides this, more specific data regarding MVDB8 and other related data for the offices need to be taken up for deeper analysis. An outline of it has been made in Sec 2.3).It can be started with mouza-types and conversion ratio data.(Sec 2.3.1)

It is worth mentioning here that DoRSR maintains a database of all the mouzas9 of the State which are listed PS-wise as well as District-wise. ADSRs further group the mouzas under their respective jurisdictions into different types.

Apart from these there is Corporation mouza type(CCC)for Corporation area.

All these types have their own set of conversion ratios(see Table 2.7).10. It has been found that ROs often are not aware of the grouping of mouza-types of their respective offices and existence of highly anomalous numbers in the database is not improbable- which may lead to irregularities in the database.

Hence, this data needs to be worked upon for detection of any possible anomaly (Please look up to Sec2.3.1.1).

Similarly Conversion Ratios of the offices can be taken up. And summarising the number of groups, officewise, any possible anomaly can be detected - as exitence of any high number of groups for an office might point toawards aberration. (Please refer to Sec 2.3.1.2).

However, streamlining of conversion Ratios for any ADSR office should be taken up only after properly exploring the prevailing practices of use of different ratios over a span of three financial years- as for even a subtle change without adjusting the basevalue untoward discrepancies may creep in the MVDB.

Then registration data of the selected offices can be taken up for assessment of their prevailing registration practices (Sec 2.3.2).

This would

Also there is scope for combining other related data, available on different platforms, and exploring them in the context of registration data. These data- besides the data coming of survey11 for market value can be helpful in building Market value models envisaged by ‘Scientific Framework’.

Workflow for optimised use of Analytics in DoRSR

Figure 2.1: Workflow for optimised use of Analytics in DoRSR

2.1 Categorisation of offices:

Categorisation of offices is neceesary for primary identification of the growth zones/offices and to render relative importance to them for informed decision making.

And for this purpose offices are put into different categories in terms of revenue ( A+++,A++,A+,A,B,C,D,E ) they earn in a fiscal year, as well as in terms of number of documents they register during that time ( A** , A*, A,B & C** ) (See Table 2.1)

It would be worth-mentioning here that before starting of computerised system - offices were categorised into A,B and C types based on document numbers - where A type registerd more than 8000 documents, B type 4000 to 8000 and C type - less than 4000 respectively.Considering the increase in the number of 8000+ offices in the mean time,12 it is convenient to divide the erstwhile A type into two more categories (Table 2.1) for coducting more coherent analysis of them- as these types generally tend to yield more revenue also.(They earned almost 54% of the total revenue in 2018-19 and 2019-20.)

Table 2.1: Revenuewise and Documentwise Office Categorisation
Revenuewise
Registered Doumentwise
Revenue_category Annual_Revenue Doc_Category Yrly_Registration
A+++ > 100 Cr A** > 12000 & < 20000
A++ > 50 Cr & < 100 Cr A* > 10000 & < 12000
A+ > 30 Cr & < 50 Cr A > 8000 & < 10000
A > 15 Cr & < 30 Cr B > 4000 & < 8000
B > 8 Cr & < 15 Cr C < 4000
C > 5 Cr & < 8 Cr
D > 3 Cr & < 5 Cr
E < 3 Cr

2.2 Regular Revenue cum Performance Analysis:

Revenue data of all the registration offices for 2018-19 , 2019-20 and 2020-21 have been assembled and offices were categorised in the manner described in Sec 2.1.

However, depending on the parameters set (ref Table 2.1), categories of the offices may change from one fiscal year to another(see Table 2.2). Tracking the changes would be the key to decide where more emphasis should be put.

2.2.1 Categorywise:

2018-19
2019-20
2020-21
Table 2.2: Number of offices in different revenue category and their revenue contribution to total revenue
Type num rev prop
A+++ 8 15079127120 28.01
A++ 17 11761615215 21.85
B 66 7223570923 13.42
A 35 7191208261 13.36
A+ 18 7017408782 13.03
C 57 3654100313 6.79
D 39 1542306716 2.86
E 16 370354590 0.69
Type num rev prop
A+++ 9 16435839638 28.26
A++ 20 13849659501 23.81
A 40 8134102173 13.99
B 74 8028047481 13.80
A+ 17 6750616236 11.61
C 52 3292441498 5.66
D 34 1384558362 2.38
E 10 224066410 0.39
Type num rev prop
A++ 20 13714873618 26.31
A+++ 6 10664204065 20.45
A 40 8343154131 16.00
B 75 8287972706 15.90
A+ 16 6030239873 11.57
C 49 3221154498 6.18
D 40 1661589086 3.19
E 10 204675799 0.39

From table above and Fig 2.2 it can be found,

  • Around 25-30 offices13 of A+++ and A++ categoies contribute around 50% of the State’s revenue.
  • And
    • 18 odd offices of A+ category,
    • 35 odd offices of A category contribute around 13% to 16% for each respectively.
  • In 2019-20 these offices fared well in general and increase in the number of offices in higher categories w.r.t 2018-19 explains it- as more numbers entered into the higher categories.
  • A+++ and A++ categories earned less in 2020-21 and number of offices in these categories decreased w.r.t 2019-20.
  • These contribute to their next lower levels.
  • Generally, A+++, A++ and A+ categories are more prone to the vagaries of external effects and policy decisions, and suffer or thrive most during such situations.
  • Also, A+++ and A++ offices follow a general trend of growing or shrinking at the expense of other offices having common/similar jurisdiction.
  • B,C type offices are more numerous in numbers (70 and 60 respectively, on average) and their shares to State revenue vary between 13% to 16% and around 6% respectively.
Diffrent Revenue Categories and Their Contributions to State Revenue:2018-19 to 2020-21

Figure 2.2: Diffrent Revenue Categories and Their Contributions to State Revenue:2018-19 to 2020-21

More insights regarding these can be found from the analysis of sections follows.

2.2.2 Division/zone:

West Bengal has five administrative divisions, namely,

  • Presidency: 5 Districts: Howrah,Kolkata,Nadia,North 24 Parganas, South 24 Parganas;

  • Medinipur: 5 Districts: Purba Medinipur,Paschim Medinipur, Purulia,Bankura,Jhargram;

  • Burdwan: 4 Districts: Paschim Burdwan, Purba Burdwan, Birbhum,Hooghly;

  • Malda: 4 Districts: Malda, Murshidabad, Uttar Dinajpur, Dakshin Dinajpur;

  • Jalpaiguri : 5 Districts: -Darjeeling, Jalpaiguri, Coochbehar, Alipurduar,Kalimpong

The categories (see Tab 2.1) can be further analysed administrative division wise, as shown in Table 2.3 and in Fig 2.3.

These show that

  • All the A+++ offices are in Presidency division: and their average distance from State Capital is 4.6 km;

  • Presidency division have

    • 14 A++, 7 A+ and 10 A category offices;
    • with avg dist 14.9 km,43.1 km and 47.2 km from State capital respectively;
    • and contributing,on average, 25.42% of total revenue among them;
  • That is, these are either in Kolkata and in its immediate surroundings (A++ type) or in its urbanised suburbs (A+ and A types) - distances of A and A+ types suggest that most of them cater to a agriculture base which are getting rapidly urbanised.

  • It is worthmentioning that these offices (as well as the similar types of Hooghly District in Burdwan division ) cover the areas where maximum number of census towns or urbanised centres have come up during 2001 to 2011- a trend which must have accelarated further during the next decade .14

  • B type offices of Presidency division are mainly of more agro-based semi-urban areas generally further away from State capital- although well-connected by multiple mode of communication . These offices contribute 4.56 % of state revenue on average.

  • C and D type offices of Presidency division- are of predominantly agararian background- however well connected with State Capital- (about 3-4 hours of travelling).These two types contribute about 2% of State revenue.

  • A++ and A+ categories of other divisions usually surround the places of strategic importance -industry hubs, important communication junctions, tourist spots.

  • ‘A’ category offices of other divisions are mostly district or sub-division headquarters where rapid urbanisation spills over the age-old administrative boundaries.15.

  • B category offices of other divisions are generally of semi-urban areas having agri-hinterland and they usually sit over important communication networks. Together their average revenue share is 9.59%.

  • It is worth-mentioning that A,B,C and D category offices of Malda division - 46 in total, are found to be more or less insular to the vagaries of external influences like pandemic. These offices register 22.07% of total documents registered in the state and together they contribute 8.02% of state revenue.

  • Keeping in view of the fluctuations in revenue generated by the A+ and upward category offices (covering established growth centres as discussed above) , much coherent focus need to be laid upon the A and B category offices to taper of possible revenue coming out of the growth centres around it in structured and logical manner.

Table 2.3: Division wise Number of Different Revenue Category Offices
Div Category Num Dist Gr1 Gr2 RevShr DocShr
Presidency A 10 47.2 3.79 -10.79 3.84 4.42
Presidency A+ 7 43.1 8.71 -13.07 4.82 3.48
Presidency A++ 14 14.9 5.30 -7.74 16.70 7.33
Presidency A+++ 9 4.6 4.40 -20.08 27.62 5.60
Presidency B 23 83.0 4.68 -6.81 4.55 9.20
Presidency C 12 70.2 2.32 -6.90 1.41 3.53
Presidency D 6 60.8 16.59 1.54 0.42 1.07
Medinipur A 11 171.8 10.91 -10.74 3.77 5.10
Medinipur A+ 2 104.5 25.06 -18.35 1.15 1.37
Medinipur B 14 147.8 15.59 -3.27 2.42 5.35
Medinipur C 12 156.6 8.64 7.49 1.39 3.23
Medinipur D 10 191.6 14.12 16.61 0.79 1.83
Medinipur E 5 229.4 6.03 -2.99 0.19 0.54
Malda A 5 330.2 8.87 0.08 1.86 3.64
Malda A+ 1 330.0 20.15 -20.54 0.67 1.03
Malda A++ 1 200.0 17.28 1.27 1.12 1.08
Malda B 21 296.2 12.42 4.22 4.17 11.87
Malda C 15 284.4 17.32 4.77 1.61 5.15
Malda D 5 363.6 12.82 7.90 0.36 1.38
Jalpaiguri A 5 675.2 20.76 -7.06 1.64 1.98
Jalpaiguri A+ 1 570.0 13.13 -4.16 0.83 0.23
Jalpaiguri A++ 2 564.5 11.13 -27.66 2.79 1.03
Jalpaiguri B 3 744.7 9.25 -11.79 0.51 0.89
Jalpaiguri C 4 656.0 0.70 37.11 0.54 0.59
Jalpaiguri D 6 671.7 2.47 4.70 0.44 0.71
Jalpaiguri E 4 654.2 0.12 12.63 0.17 0.36
Burdwan A 9 127.7 15.82 6.77 3.02 3.66
Burdwan A+ 6 84.2 7.52 -11.98 3.92 3.17
Burdwan A++ 3 171.0 15.15 -8.71 3.26 1.78
Burdwan B 13 100.7 9.71 -2.63 2.47 4.93
Burdwan C 9 149.0 7.46 -6.40 0.99 2.89
Burdwan D 7 123.6 13.96 2.15 0.49 1.50
Burdwan E 1 147.0 1.77 -15.90 0.05 0.07
a Div = Administrative Divisions: namely,
Presidency: 5 Districts: Howrah,Kolkata,Nadia,North 24 Parganas, South 24 Parganas;
Medinipur: 5 Districts: Purba Medinipur,Paschim Medinipur, Purulia,Bankura,Jhargram;
Burdwan: 4 Districts: Paschim Burdwan, Purba Burdwan, Birbhum,Hooghly;
Malda: 4 Districts: Malda, Murshidabad, Uttar Dinajpur, Dakshin Dinajpur;
Jalpaiguri : 5 Districts: Darjeeling, Jalpaiguri, Coochbehar, Alipurduar,Kalimpong
b Category = Revenue Category: for it F.Y. 2019-20 has been taken up
c Num = Number of Registration offices in the respective category in that division
d Dist = Average distance in Km from State Capital
e Gr1 = Average Revenue Growth of the respective category in 2019-20 w.r.t 2018-19
f Gr2 = Average Revenue Growth of the respective category in 2020-21 w.r.t 2019-20
g rev_shr = Respective contribution towrads State Revenue: averaged over F.Y.s 2018-19,2019-20,2020-21
h doc_shr = Respective contribution towards Total number of documents registered in the state: averaged over 3 F.Y.s 2018-19 to 2020-21
Revenue Analysis:Division wise

Figure 2.3: Revenue Analysis:Division wise

Fig 2.3 contains two graphs- namely,

  • Revenue Categories:1:Numbers,Distance From SHQ & Revenue%;
  • Revenue Categories:2:Categories,Numbers & Revenue%;
  • First one plots different revenue categories division-wise as dots on a distance scale- average distance from state capital in km.The size of the dots depicts the respective revenue contributions of the respective categories. THe number of offices in the particular category are shown besides the respective dots.
  • Second one plots different revenue categories division-wise and their respective numbers in columns. Respective revenue-contributions are shown besides each column.

2.2.3 District/office wise:

2.2.3.1 Districtwise:

Analysis of districtwise revenue then can be done to get an overall picture of the performances. Table 2.416 and Figures 2.417,

2.518 render necessary insights.
Table 2.4: District wise Revenue etc. data of the State
District Rev1 Doc1 Rev2 Doc2 Rev3 Doc3 RvGr1 DocGr1 RvGr2 DocGr2 rvshr docshr
North 24 Pgs 9099109502 166273 10359659124 169756 8620868655 139679 13.8 2.09 -16.8 -17.7 17.11 10.53
South 24 Pgs 9796915300 168612 9582419081 166082 8162951031 132864 -2.2 -1.50 -14.8 -20.0 16.79 10.35
Kolkata 8638565070 39521 8750639700 37972 7656368001 30458 1.3 -3.92 -12.5 -19.8 15.26 2.39
Howrah 3314022644 65140 3577384405 65208 3022909976 56540 8.0 0.10 -15.5 -13.3 6.04 4.14
Hooghly 2891822677 90529 3155666187 91062 2933136319 80928 9.1 0.59 -7.0 -11.1 5.47 5.81
Murshidabad 2473640384 202548 2785823500 190901 2878197961 183612 12.6 -5.75 3.3 -3.8 4.96 12.78
Nadia 2139596813 110470 2367387903 112439 2299628451 102809 10.6 1.78 -2.9 -8.6 4.15 7.21
Purba Medinipur 2104400057 121110 2471051527 127736 2225178928 108886 17.4 5.47 -9.9 -14.8 4.14 7.92
Paschim Bardhaman 1721325216 29580 1912470170 29555 1782985575 24242 11.1 -0.08 -6.8 -18.0 3.30 1.85
Purba Bardhaman 1635639149 79265 1818073330 76687 1792965203 69686 11.2 -3.25 -1.4 -9.1 3.20 5.00
Paschim Medinipur 1575007081 84979 1734175528 86585 1797313742 79014 10.1 1.89 3.6 -8.7 3.11 5.55
Malda 1338780400 90081 1502651419 89951 1474247771 84581 12.2 -0.14 -1.9 -6.0 2.63 5.86
Darjeeling 1408965395 18643 1570927838 17692 1327486323 14270 11.5 -5.10 -15.5 -19.3 2.63 1.12
Jalpaiguri 1293957702 24310 1372519715 24501 1216647438 20613 6.1 0.79 -11.4 -15.9 2.37 1.54
Birbhum 1088086163 86694 1293050290 81410 1271861536 73776 18.8 -6.10 -1.6 -9.4 2.23 5.36
Coochbehar 753572656 38726 893092112 40180 818863719 34706 18.5 3.75 -8.3 -13.6 1.50 2.52
Uttar Dinajpur 716261832 50129 822597465 52777 850584039 42475 14.8 5.28 3.4 -19.5 1.46 3.22
Bankura 730258664 32462 799609746 33200 700004964 32394 9.5 2.27 -12.5 -2.4 1.36 2.17
Purulia 429052948 20892 460253298 20803 445389144 18625 7.3 -0.43 -3.2 -10.5 0.81 1.34
Dakshin Dinajpur 356568290 35097 438897467 35549 428151202 33207 23.1 1.29 -2.4 -6.6 0.75 2.30
Alipurduar 180290305 7875 238320997 8177 224431851 7256 32.2 3.83 -5.8 -11.3 0.39 0.52
Jhargram 127899241 6410 173119562 6853 178426179 6675 35.4 6.91 3.1 -2.6 0.29 0.44
Kalimpong 25954435 1293 19540939 1389 19265768 1403 -24.7 7.42 -1.4 1.0 0.04 0.09
a Rev1 = Revenue in F.Y. 2018-19
b Rev2 = Revenue in F.Y. 2019-20
c Rev3 = Revenue in F.Y. 2020-21
d Doc1 = Number of Documents registered in 2018-19
e Doc2 = Number of Documents registered in 2019-20
f Doc3 = Number of Documents registered in 2020-21
g RvGr1 = Revenue Growth in 2019-20 w.r.t 2018-19
h RvGr2 = Revenue Growth in 2020-21 w.r.t 2019-20
i DocGr1 = Document Growth in 2019-20 w.r.t 2018-19
j DocGr2 = Document Growth in 2020-21 w.r.t 2019-20
k rvshr = Percentage of Revenue contributed towards the state total- averaged over 3 F.Y.s- 2018-19 to 2020-21
l docshr = Percentage of the Number of documents registered to the state’s total number: averaged over 3 F.Y.s- 2018-19 to 2020-21
Revenue and Document Share of the Districts:2018-19 to 2020-21

Figure 2.4: Revenue and Document Share of the Districts:2018-19 to 2020-21

Revenue Growth of Districts:2019-20 to 2020-21

Figure 2.5: Revenue Growth of Districts:2019-20 to 2020-21

These show

  • quite expectedly (Please refer to discussions in Sec 2.2.2)- North 24 Parganas, South 24 Parganas, Kolkata (or RA_kolkata), Howrah, Hooghly are top five revenue contributors. Together they put up about 60% of the total revenue ( Graph 1 of Fig 2.4);

  • while, Murshidabad,North 24 Parganas, South 24 Parganas, Purba Medinipur, Nadia, Malda are the top contributors towards total number of documents registered annually.Together they share around 55% of the total numbers (Graph 2 of Fig 2.4);

  • that In 2019-20 most of the districts fared well and only four districts fell below the state growth of 7.91% (Graph 1 of Fig 2.5);

  • while in 2020-21 only four districts, namely Murshidabad, Paschim Medinipur, Jhargram, Uttar Dinajpur-together which contribute only 10% of the total revenue (Uttar Dinajpur’s share is 1.51% and Jhargram’s 0.3%)- shown positive growth, and excepting Hooghly, all the top five revenue earning districts fell below the state growth of -10.26% (Graph2 of Fig 2.5).

2.2.3.2 Officewise:

Likewise, similar exercises can be taken up for offices.

  • Table 2.5 has been prepared by picking up State’s top revenue earning offices and sorting them in descending order of annual revenue they earned.

  • For sorting F.Y 2019-20 has been considered as most of the top offices fared well in that year.

  • Also revenue, number of documents for last three financial years, revenue,document-growth for the corresponding years have been taken into account.

  • Figures 2.6 and 2.7 show the respective revenue collected at these offices during 2018-19,2019-20 and in 2020-21.

Table 2.5: High yielding Revenue Offices
Distr Offc dist rev1819 rev1920 rev2021 revgr1 docgr1 revgr2 docgr2
Kolkata ARA-IV 1 3090473570 2822566440 1877468684 -8.67 -6.37 -33.5 -36.71
North 24 Pgs ADSR Rajarhat 17 2419612699 2661522967 1840533296 10.00 5.57 -30.9 -30.55
Kolkata ARA-III 1 710176656 2447448710 2279198517 244.63 -15.56 -6.9 -8.43
Kolkata ARA-II 1 1680205201 1748495662 1268518799 4.06 91.22 -27.4 -29.12
Kolkata ARA-I 1 3157709643 1732128888 2231182001 -45.15 -32.56 28.8 6.02
North 24 Pgs ADSR DumDum 7 1146724015 1501952464 1167302768 30.98 8.59 -22.3 -28.25
South 24 Pgs ADSR Sealdah 3 994012034 1185823745 854304168 19.30 -9.91 -28.0 -26.59
South 24 Pgs ADSR Alipore 5 1442220130 1181069168 698916033 -18.11 -17.59 -40.8 -54.14
Howrah ADSR Howrah 5 1101457326 1154831594 917804673 4.85 -5.52 -20.5 -16.93
South 24 Pgs DSR-II South 24-Parganas 5 1040724537 982038481 996999578 -5.64 -6.38 1.5 -23.36
Jalpaiguri ADSR Bhaktinagar 574 827280498 898590359 679124248 8.62 3.43 -24.4 -32.49
North 24 Pgs ADSR Bidhannagar 9 616492020 875302782 641597404 41.98 32.76 -26.7 -27.08
South 24 Pgs ADSR Behala 12 792624591 857197043 846843584 8.15 4.70 -1.2 -10.07
Darjeeling ADSR Bagdogra 555 744236136 847811045 584190403 13.92 -0.74 -31.1 -37.72
South 24 Pgs DSR-III South 24-Parganas 5 880674486 789072212 738717948 -10.40 -7.14 -6.4 1.19
Howrah DSR-II Howrah 5 675866377 757032189 741429243 12.01 1.58 -2.1 10.92
North 24 Pgs ADSR Sodepur 19 641386488 711409736 740134689 10.92 2.68 4.0 -12.06
Paschim Bardhaman ADSR Durgapur 185 586530009 709141931 622286259 20.90 7.00 -12.2 -12.22
Paschim Bardhaman ADSR Asansol 225 606695304 683903972 622286259 12.73 -4.65 -9.0 -28.45
Murshidabad ADSR Sadar Murshidabad 200 548744131 643593931 651747841 17.28 -1.54 1.3 0.08
South 24 Pgs ADSR Garia 17 512178418 637753266 532037798 24.52 8.66 -16.6 -15.71
Howrah DSR-I Howrah 5 425018764 610746547 451179167 43.70 32.02 -26.1 -25.77
North 24 Pgs ADSR Barasat 25 623532245 608010542 545666234 -2.49 -10.29 -10.2 -19.86
South 24 Pgs DSR-IV South 24-Parganas 5 690377647 600550294 718417794 -13.01 -8.66 19.6 2.26
North 24 Pgs DSR-I North 24-Parganas 25 389707012 555215084 491030399 42.47 28.36 -11.6 -14.82
North 24 Pgs DSR-III North 24-Parganas 25 458685293 531444626 539227113 15.86 16.07 1.5 -15.71
Purba Bardhaman ADSR Sadar Purba Burdwan 103 475166722 528098384 509331744 11.14 -5.01 -3.5 -19.38
North 24 Pgs ADSR Naihati 47 513558441 515645553 533810605 0.41 -11.30 3.5 -5.72
South 24 Pgs DSR-I South 24-Parganas 5 797249737 507101526 283323329 -36.39 -17.70 -44.1 -39.87
a dist = Distance from State caital/State HQ,in Km
b rev1819 = Revenue in 2018-19
c rev1920 = Revenue in 2019-20
d rev2021 = Revenue in 2020-21
e revgr1 = Revenue growth in 2019-20 w.r.t 2018-19
f revgr2 = Revenue growth in 2020-21 w.r.t 2019-20
g docgr1 = Document growth in 2019-20 w.r.t 2018-19
h docgr2 = Document growth in 2020-21 w.r.t 2019-20
Top Revenue Earning offices :2018-19

Figure 2.6: Top Revenue Earning offices :2018-19

Top Revenue Earning offices in 2019-20 and 2020-21

Figure 2.7: Top Revenue Earning offices in 2019-20 and 2020-21

These show

  • that the 100 Cr+ (or A+++ category as discussed earlier) offices are situated within 15-20 km of the state headquarters and include RA offices and several offices of Alipore, and ADSRs Rajarhat, Sealdah, Dumdum (ADSR Cossipore Dumdum - to be more specific);

  • that the rest offices (mostly A++ categories) fall mostly in Kolkata’s suburb districts such as North 24 Parganas, South 24 Parganas, Howrah ; as well as in the prominent growth areas like Asansol-Durgapur industrial zone (ADSR Asansol,ADSR Durgapur) and Siliguri(ADSR Bhaktinagar in Jalpaiguri district and ADSR Bagdogra, ADSR Siliguri in Darjeeling district) and in some District head quarters over important communication networks (ADSR Burdwan Sadar, ADSR Murshidabad Sadar).

Further revenue-growths of these offices can be studied by plotting them in Figure 2.8 (contains two plots,the second one been adjusted by keeping out ARA-III-whose high number dwarfed the others) for 2018-19 and in Figure 2.9 - for 2020-21 respectively.

Revenue Growth of Top Offices in 2019-20

Figure 2.8: Revenue Growth of Top Offices in 2019-20

Revenue Growth of Top Offices in 2020-21

Figure 2.9: Revenue Growth of Top Offices in 2020-21

Findings are as follows-

In 2018-19. (Please refer Fig 2.8)

  • ARA-III - even with negative document growth (-15.56 %) showed revenue growth of a stellar 244.63%;
  • ARA-II’s document growth was 91% and revenue grew at 4.06 %;
  • ARA-IV’s document growth was -6.37% and revenue growth -8.67%;
  • ARA-I’s document growth was -32.56% and revenue growth -45.15%; It is worth-mentioning that during this period - jurisdiction of individual ARA offices were lifted and during first phases of implementation system by itself would pick up the offices - which however was sorted out in very short time.

Nevertheless, few notable things happened around this-

  • It led to the exodus of revenue from ARA offices towards suburb offices.19
  • Offices like ADSR Dumdum (ADSR Cossipore Dumdum, more specifically), ADSR Bidhannagar, ADSR Sealdah, ADSR Garia, DSR-I & DSR-II Howrah, ADSR Sodepur seems to have benefited most in this situation;
  • Among ARA offices - ARA III seems to have registered more documents involving property transfer instead of documents involving other than immovable property that it would do earlier.
  • A large chunk of latter kind of documents seems to have been registered at ARA-II;
  • However, offices at Alipore could not reap the benefits.
Hence it can be easily inferred that ARA offices, offices at Alipore, ADSR offices Rajarhat, Sealdah, Dumdum, Behala, Garia, Barasat, 20 Sodepur, Naihati and offices at Howrah Sadar - share among themselves the revenue of greater Kolkata urban area. These offices have a tendency to grow or shrink at the expense of the others having jurisdiction common in themselves.21.Generally, far the office is from Kolkata - sharing of revenue with ARA offices decreases.22.

Apart from these offices, ADSRs Durgapur, Berhampore, Asansol, Bagdogra, Burdwan, Bhaktinagar showed positive revenue growth.

In 2020-21 (Please refer Fig 2.9)

  • Pandemic took its toll and most of these offices showed negative revenue growth.
  • Only ARA- I showed significant positive growth. Decreased revenue of ARA-IV seems to have been distributed among ARA-I and ARA-III.
  • Meanwhile jurisdiction among the DSRs in the districts has been lifted in this financial year. It seems to have benefited DSR-IV South 24 Parganas during this period.

It is found from the above analysis that revenue of top offices in Kolkata and its immediate suburbs are more or less distributed among themselves. With Covid looming above and demand shrinking, it is more or less becoming saturated. 23 Hence it is high time that DoRSR looked beyond these offices and

  • Starts thorough analysis of the next important levels, one by one, and examine regularly the registration practices prevalent in these offices,

  • analyzes how they prepare their MV and other related database,

  • and guiding them

    • how efficiently those can be handled by employing lesser subjectivity;
    • and the growth centers be identified properly and revenue from them can be tapered of.

2.3 Deeper Analysis of Registration data for select zone or office:

Please refer to the discussion in Sec 2. The rationale for taking up deeper analysis of database of individual offices has been discussed there. To reiterate, it will help in identify the anomalies present in the database

  • by analysing the mouzatypes and conversion ratios-
    • where districtwise and PSwise data of different mouza types would be grouped and summarised and their respective numbers are to be checked- primarily offices having any unusually high number, specially in ‘Developing’ type in comparison with others in relatively not very developed zone , can be picked up for further analysis;
    • and subsequently conversion ratios are to be picked up and to be grouped and summarised districtwise and officewise. Like mouza-type grouping, here also grouping may reveal presence of unusually high number of groups -
    Such offices can be taken up and registration data of these offices are to be examined to explore the registration practices which might point towards unintentional practices of using different ratios/usages even for similar type of transfers. Also registration pattern of particular offices can be explored by analysing the seasonal and spatial distribution of transfers. It would help taking informed decision for administrative purposes and for focussing on the growth zones more logically.

2.3.1 Analysis of Mouza types and Conversion Ratios:

As discussed mouza-types and conversion ratios are taken up for grouping and summarising.

2.3.1.1 District and PS wise summarsing of different mouzatypes:

All the mouzas have been grouped under respective Districts, PSs and numbers of different types, namely ‘Rural’,‘Developing’etc (Please refer to the discussion in Sec 2) and have been summarised in table 2.6 - specially the proporionate number of ’Developing’ mouzas has been taken into consideration. Any high number, specially in not so developed zones may be picked up.Table 2.6 tesifies presence of such and can be supported by Fig 2.10.
Table 2.6: Analysis of District & PS wise Mouza Types
Dist PS TotMouz Rrl Dev Muni Muni1 Muni2 Muni3 Dev_prop
South 24 Parganas Kolkata Leather Camp 31 0 31 0 0 0 0 100
Purba Bardhaman Ketugram 122 7 114 1 0 1 0 93
Howrah Sankrail 40 3 35 2 0 2 0 88
Hooghly Magra 52 0 45 7 1 6 0 87
Darjeeling Matigara 57 7 49 1 1 0 0 86
Nadia Kaliganj 127 24 103 0 0 0 0 81
Nadia Nakashipara 109 23 86 0 0 0 0 79
Hooghly Polba 97 21 76 0 0 0 0 78
North 24 Parganas Rajarhat 158 28 109 21 14 7 0 69
North 24 Parganas Amdanga 81 27 54 0 0 0 0 67
Murshidabad Kandi 101 31 59 11 5 3 3 58
Purba Medinipur Nandakumar 101 44 57 0 0 0 0 56
Howrah DOMJUR 55 25 30 0 0 0 0 55
Darjeeling Kurseong 137 57 74 6 1 5 0 54
North 24 Parganas Titagarh 15 2 8 5 5 0 0 53
Coochbehar Haldibari 62 27 32 3 3 0 0 52
South 24 Parganas Budge Budge 34 4 17 13 7 6 0 50
Dakshin Dinajpur Tapan 279 141 138 0 0 0 0 49
Hooghly Balagarh 136 69 67 0 0 0 0 49
Howrah Panchla 33 17 16 0 0 0 0 48
a Dist = District Name
b PS = Police Station Name
c TotMouz = Total Number Mouzas in that PS
d Rrl = Total Number of Rural Mouzas in that PS
e Dev = Total Number of Developing Mouzas in that PS
f Muni = Total Number of Municipality Mouzas in that PS
g Muni1 = Total Number of Municipality Type 1 Mouzas in that PS
h Muni2 = Total Number of Municipality Type 2 Mouzas in that PS
i Muni3 = Total Number of Municipality Type 2 Mouzas in that PS
j Dev_prop = Proprtion of Developing mouzas to Total Number of Mouzas in that PS
k Rows have been created in the descending order of Dev_proportion in the state and only the top 30 have been shown here.

From Table above, it is found that there are many police stations in the state with high ‘Developing’ to ‘Total’ mouza ratio ( here, Developing_Proportion ).

  • However, keeping in view of the level of development for PSs like ‘Kolkata Leather Complex’,‘Sankrail’,‘Rajarhat’,‘Domjur’ presence of such high numbers can be explained.
  • But, for PSs like
    • Ketugram(Number of Developing Mouzas 114 out of total 122 ),
    • Magra(45 out of 52) ,
    • Kaliganj(103 out of 127),
    • Nakashipara(86 out of 109),
    • Kandi(59 out of total 158 of which 11 are Municipality mouzas),
    • Nandakumar(57 out of 101 ),
    • Haldibari(32 out of total 62 of which 3 are Municipality mouzas),
    • Balagarah(67 out of 136),
    • Tapan(138 of 279),
    • Swarupnagar(30 out of 66), it seems unlikely24 and warrants for further study of the Conversion Ratios in general and that of practices around their usages during registration.
PSs Having High Proportion of Developing Mouzas to Total

Figure 2.10: PSs Having High Proportion of Developing Mouzas to Total

2.3.1.2 District and Officewise summarising of Ratios:

Generally speaking,‘Conversion Ratio’ is the list of factors assigned for all landgroups and mouza-types of a ADSR office. Usually all the land-types procured from ‘Land Department’ are put into different categories or land-groups by ADSRs based on the similarity of usages. Each land-group has its own set of multiplicative factors. 25.Table 2.7 is an example of the format as to how conversion ratios of ADSR offices are stored.

Table 2.7: Example of a Conversion Factor Table:ADSR BANKURA
Dtcd Dt Rcd offnm uscd clcd class rrl dev m1 m2 m3 crp
01 Bankura 02 A.D.S.R. BANKURA 001 01 Shali 1.0000 1.0000 2.0000 2.0000 2.0000 NULL
01 Bankura 02 A.D.S.R. BANKURA 002 03 Suna 2.0000 1.0000 2.0000 2.0000 2.0000 NULL
01 Bankura 02 A.D.S.R. BANKURA 003 04 Doe 3.0000 1.0000 2.0000 2.0000 2.0000 NULL
01 Bankura 02 A.D.S.R. BANKURA 004 01 Baide 1.0000 1.0000 2.0000 2.0000 2.0000 NULL
01 Bankura 02 A.D.S.R. BANKURA 005 02 Kanali 1.5000 1.0000 2.0000 2.0000 2.0000 NULL
01 Bankura 02 A.D.S.R. BANKURA 008 02 Sole 1.5000 1.0000 2.0000 2.0000 2.0000 NULL
01 Bankura 02 A.D.S.R. BANKURA 457 23 Danga Land 6.0000 3.0000 2.0000 2.0000 2.0000 0.0000
01 Bankura 02 A.D.S.R. BANKURA 013 08 Bastu 12.0000 4.0000 2.0000 2.0000 2.0000 NULL
01 Bankura 02 A.D.S.R. BANKURA 456 07 Vitti/Vitta 12.0000 4.0000 2.0000 2.0000 2.0000 NULL
01 Bankura 02 A.D.S.R. BANKURA 018 10 Khamar 10.0000 3.5000 2.0000 2.0000 2.0000 0.0000
a Dtcd = District Code
b Dt= District
c Rcd= RO Code
d offnm = Office Name
e uscd = Land Use Code - prepared at DoRSR based on the grouping proposed by ADSRs
f clcd = Land Class Code - maintained by Land department
g class = Land Class supplied primarily by Land Bepartment. On few occasions ADSRs have also introduced few classes.
h rrl = Conversion Ratio for Rural Mouzas
i dev = Conversion Ratio for Developing Mouzas
j m1 = Conversion Ratio for Municipality1 Mouzas
k m2 = Conversion Ratio for Municipality2 Mouzas
l m3 = Conversion Ratio for Municipality3 Mouzas
m crp = Conversion Ratio for orporation Mouzas : Applicable for Corporation area of KMC, HMC, BMC etc.
n For discussion on grouping of mouzas, please refer to Sec 2: Approach

Market value of a property is determined by multiplying the basevalue of that particular property and the conversion factor assigned for that particular landgroup(of that very mouza-type) to which its landtype belongs. ROs determine the proposed use of that particular property by employing their field knowledge and use the respective conversion ratio to reach at the market value of that property- which leaves an ample scope of subjectivity.

So a study of the officewise number of landgroups needs to be undertaken and it is to be reconciled with the registration data of the offices in regular interval to find out whether the landgroups (and in turn conversion ratios) are actually being utilised properly.

Table 2.8, Figure 2.11 and Figure 2.12) have been prepared for this purpose.

  • Table Table 2.8 has two separate tables T1 and T2.

    • T1 shows districtwise list of median26number of landgroups.

    • T2 lists the number of landgroups and the number of offices having that particular number of landgroups (or set of conversion ratios) and what proportion is that number to the toatal number of offices of State.

  • Figure 2.11 summarises the number of offices with corresponding number of landgroups.

  • Figure 2.12 plots all the offices alongwith their corresponding number of landgroups.With the help of boxplots, it also plots the distribution of the number of landgroups within a district across its offices.

From these, it is found that

  • ADSR offices do have a lot of variations in the number of landgroups- starting from 2 to 62. Figure 2.12).
  • Median number of landgroups for all offices of state is 13.
  • There are 49 offices (21.74%) with landgroups more than 20. (Table 2.8)
  • There are 74 offices (32.87%) with landgroups less than or equal to 10,
  • And, 109 offices (48.46%) have landgroups 11-20.
  • 20 offices for each have 11 and 15 landgroups.(Fig 2.11).
  • In general,offices of the districts Purulia (24), Murshidabad (21), Purba Bardhaman (18) , Kalimpong(17),Paschim Medinipur(17) and Nadia(16) have generally much higher number of land groups.
Number of Land groups and Number of Offices

Figure 2.11: Number of Land groups and Number of Offices

Table 2.8 below captures the a picture of diffent number of landgroups,number of offices and a summary of district wise distribution of the number of landgroups.

It has two parts.

  • In one Part 1 it shows district wise median number of landgroups.
  • And Part 2 summarises different number of landgroups along with respective number of offices having it.
Part 1 :District wise Median Number of Landgroups
Part 2:Number of Land groups and Number of offices
Table 2.8: Landgroups summary
District Mdlg
Purulia 24
Murshidabad 21
Purba Bardhaman 18
Kalimpong 17
Paschim Medinipur 17
Nadia 16
Bankura 15
Darjeeling 15
Hooghly 15
Paschim Bardhaman 15
Birbhum 14
Jhargram 14
Malda 14
Purba Medinipur 13
Jalpaiguri 12
North 24 Parganas 12
Alipurduar 10
Howrah 10
South 24 Parganas 10
Coochbehar 8
Uttar Dinajpur 8
Dakshin Dinajpur 7
Kolkata 2
Num_of_landgroups Num_of_offc proportion
11 20 8.89
15 20 8.89
10 19 8.44
8 16 7.11
9 16 7.11
7 13 5.78
16 13 5.78
13 10 4.44
12 9 4.00
14 8 3.56
17 8 3.56
18 8 3.56
20 7 3.11
21 7 3.11
19 6 2.67
6 5 2.22
23 5 2.22
25 4 1.78
26 3 1.33
5 2 0.89
22 2 0.89
24 2 0.89
27 2 0.89
28 2 0.89
29 2 0.89
34 2 0.89
36 2 0.89
2 1 0.44
3 1 0.44
4 1 0.44
30 1 0.44
31 1 0.44
32 1 0.44
33 1 0.44
35 1 0.44
46 1 0.44
58 1 0.44
60 1 0.44
62 1 0.44
1 Part 1:Mdlg = Median number of Landgroups of that District,rounded to nearest whole number
2 Part 2:Num_of_landgroups= Number of land groups
3 Part 2:Num_of_offc= Number of offices with that particular number of land groups
4 Part 2:proportion = Proportion of the Number of offices in that particular category to total number of offices
5 Explanation of Part 2:1st row has 11 in ‘Num_of_landgroups’ column and 20 in ‘Num_of_offc’ column,
it means 20 offices have 11 landgroups.
Number of Landgroups of the ADSR offices

Figure 2.12: Number of Landgroups of the ADSR offices

Table 2.9 below shows the list of few offices having higher number of landgroups.

Table 2.9: Offices with most number of land groups: Top 10 of the State
District_code Disrict ro_code office num_of_landgrps
12 Murshidabad 15 A.D.S.R. JANGIPUR 62
03 Birbhum 03 A.D.S.R. BOLPUR 60
12 Murshidabad 26 A.D.S.R. SUTI 58
14 Purulia 04 A.D.S.R. JHALDA 46
10 Paschim Medinipur 06 A.D.S.R. BALICHAK 36
16 South 24 Parganas 11 A.D.S.R. BARUIPUR 36
02 Purba Bardhaman 19 A.D.S.R. RAINA 35
14 Purulia 02 A.D.S.R. PURULIA 34
23 Paschim Bardhaman 04 A.D.S.R. RANIGANJ 34
01 Bankura 02 A.D.S.R. BANKURA 33

Primary analyses of registration data (See Sec 2.3.2) of few select offices as well as the field experience tell that - whatever be the number of landgroups in their respective databases, ROs mostly use 6-7 landgroups or conversion ratios during market-value determination . And these revolve largely around variants of ‘agricultural’,‘waterbody’,‘residential’,‘commercial’ and in some cases ‘industrial’ groups of usages.

Hence, existence of higher number of landgroups eventually increases the chance of ambiguous land-groups’ presence in the database, which in turn enhances the chance of ambiguity around market value determination.

And keeping in mind the fact that registration generally covers only around 5% of the plots of any jurisdiction and generally occurs(often with repetition) around some well-known pocketed areas(See Fig 2.15); stress should be put in to encourage the ROs to prepare the database of basevalue of properties more carefully** by catching up the ground reality of location, level of development using plot maps, satellite imageries etc, so that reliance over conversion ratios can be minimised.**

In this exercise ROs can be helped by making them aware of the registration pattern of their offices and by helping them to identify growth zones of their jurisdictions.

2.3.2 Assessment of Registration Practices for select offices:

Registration practices of the offices need to be assessed in regular interval which would enable DoRSR to guide the ROs more effectively. For it registration data of the offices is to be taken up for analysis and all the offices need to be covered in at least once in two years’ time.

Broadly these would help in

  • identification of important growth zones;
  • analysis of ‘Proposed land use’ practices;
  • assessment of registration pattern.

To illustrate this process registration data of ADSR Raniganj for f.y 2018-19,2019-20 and 2020-21(upto December’20) has been taken up.ADSR Raniganj is an important office of Paschim Burdwan district.Its annual revenue ranges between 32 Cr to 34 Cr(A+ category) with annual registration around 6600 covering 4 PSs,namely, Raniganj, Jamuria,Andal,Pandabeswar. Jurisdiction of it has marked agricultural,industrial (colliery and steel, sponge-iron) areas, reasonably developed residential ( 2 municipalities besides 48 census towns and commercial areas.Economy of this area largely depends on the colliery.

2.3.2.1 Identification of Important/Potential growth zones:

It is known fact that registration of apartments play a crucial role towards revenue of the urbanised areas - so, for identification of important growth zones within the jurisdiction, relative share of land and apartment registration also needs to be looked into - starting from the jurisdiction level then gradually to PS level and to mouza and plot level needs to be analysed.

Table 2.10 has been created to show the share of land and apartment registration for whole of the jurisdiction.

Table 2.10: Contribution of land and apartment in registration: ADSR Raniganj
L/A num_plts prop_plts prop_rv
A 2563 4.6 16
L 52894 95.4 84
1 L/A = Land or Apartment; L = Land and A = Apartment.
2 num_plts = Number of plots registered.
3 prop_plts = Proportion of plots involved.
4 prop_rv = Proportion of Revenue from respective categories

It is seen that land contributes 83.66% of revenue with 95.38% of plots registration and apartments contribute 16.34% of revenue involving 4.62% of plots registration.

Analysis of Table 2.11 and Table 2.12 in which PS wise and mouza wise data (for top 20 revenue earning mouzas) have been grouped and summarised respectively and Fig 2.15- which shows the distribution of market value across the plot numbers, will gradually show the areas where apartment registration is concentrated.

In Table 2.11 PS wise share of revenue and plots involved in registration have been summarised. Also alongside, proportionate share of rural, developing and municipalities of the respective PSs have been looked into.

Table 2.11: PS wise analysis of transaction: ADSR Raniganj
PS and Proportions
Revenue Share of Mouza Types
Revenue Share of Land /Apartment
PS prp_rv prp_plts rrl_rev_pr dev_rev_pr muni_rev_pr lnd_pr apt_pr
Raniganj 39.1 27 13 0.0 87 64 36.41
Jamuria 29.9 38 39 1.4 60 100 0.30
Andal 23.7 23 97 3.1 0 92 8.32
Pandabeswar 7.4 12 100 0.0 0 99 0.83
1 prp_rv = Proportionate Revenue share;
2 prp_plts = Proportion of number of plots trnsferred;
3 rrl_rev_pr = Contribution towards revenue of this PS of Rural mouzas of this PS;
4 dev_rev_pr = Contribution towards revenue of this PS of Developing mouzas of this PS;
5 muni_rev_pr = Contribution towards revenue of this PS of Municipality mouzas of this PS;
6 lnd_pr = Revenue proportion from land registration;
7 apt_pr = Revenue proportion from land registration;

In Fig 2.13 PS wise proportion of revenue and transferred plots have been shown.

Revenue and plots Share of PSs:ADSR:Raniganj

Figure 2.13: Revenue and plots Share of PSs:ADSR:Raniganj

From Table 2.11 and from Fig 2.13 it is found that

  • Raniganj contributes most to the revenue (39.06%);
    • of it ‘Municipality’ mouzas contribute 87% while ‘Rural’ mouzas contribute 13%.
    • Apartment registration contributes 36.41% of the revenue coming from Raniganj PS.
  • Jamuria’s revenue share is 29.92%;
    • Respective share of Rural,Developing and Municipality mouzas are 39%,1.43% and 59.6% respectively;
  • Andal’s revenue share is 23.66%;
    • Respective share of rural,developing mouzas are 96.9% and 3.14% respectively. There is municipalities in Andal PS.
    • Apartment registration contributes 8.32% of the revenue coming from Raniganj PS.
  • Pandabeswar’s share of revenue is 7.36%;
    • All of it comes from rural mouzas as there are no developing and municipality mouzas in Pandabeswar PS.
  • Jamuria registers most number of plots(37.63%), followed by Raniganj(27.31%),Andal(22.77%) and Pandabeswar(12.29%).
  • Apartment registration in Jamuria and Pandebswar is not significant.

In mouza wise summary, mouzas with respective number of plots transferred during this period, their proportionate number to that of total, proportinate revenue, average area of land transfer27,percentage of plots of that particular mouza registered for lands and for apartments, proportionate share of revenue from land and from apartment. Also it has been checked how important is the role of apartmenent registration for its jurisdiction. For this purpose, proportionate number of apartment transfers of each mouza to the total number for apartment registration of entire jurisdiction and the proportionate revenue from apartment to revenue from all apartment transfers have been checked out.(See Part IV of Tab 2.12).

In Table 2.12 top 20 mouzas which contribute most to revenue have been listed. Fig 2.14 shows these mouzas with their respective revenue and plots share.In it different mouza-types have been color-coded.

Table 2.12: Top Mouzas At a Glance: ADSR Raniganj
I:PS-Mouza
II:Share to Total
III:Distribution:Land-Apt
IV:Apt-Anls
PS Mty Mcd Mouza nPts Pr_pts Pr_rv avAR nldpts nappts nld_pr nap_pr rld_pr rap_pr Tnap_pr Trap_pr
Raniganj OM2 24 Raniganj Municipality 3189 5.8 14.8 2.7 2236 953 70 29.88 50 50.02 37.18 7.38
Raniganj OM2 17 Searsole 2284 4.1 7.8 4.3 1413 871 62 38.13 50 50.25 33.98 3.91
Raniganj OM2 18 Amrasata 1188 2.1 6.2 6.2 755 433 64 36.45 53 47.16 16.89 2.93
Andal ROO 32 Khandra 2746 5.0 5.3 5.6 2739 7 100 0.25 100 0.48 0.27 0.03
Jamuria OM1 37 Mandalpur 995 1.8 3.4 13.7 995 0 100 0.00 100 0.00 0.00 0.00
Jamuria ROO 6 Churulia 1807 3.3 3.2 11.0 1806 1 100 0.06 100 0.00 0.04 0.00
Andal ROO 18 Ukhra 895 1.6 3.0 4.4 889 6 99 0.67 99 0.82 0.23 0.02
Andal ROO 52 Andal 1063 1.9 2.1 3.6 1063 0 100 0.00 100 0.00 0.00 0.00
Raniganj RM2 13 Egara 2094 3.8 2.0 4.9 2094 0 100 0.00 100 0.00 0.00 0.00
Jamuria OM1 35 Bijpur 1101 2.0 2.0 10.3 1101 0 100 0.00 100 0.00 0.00 0.00
Andal ROO 41 Kajora 864 1.6 1.8 4.4 863 1 100 0.12 100 0.23 0.04 0.00
Andal ROO 42 Bhadur 586 1.1 1.8 3.7 478 108 82 18.43 48 52.23 4.21 0.94
Pandabeswar ROO 12 Jawalbhanga 819 1.5 1.8 16.0 819 0 100 0.00 100 0.00 0.00 0.00
Jamuria OM1 21 Jamuria 764 1.4 1.7 7.6 764 0 100 0.00 100 0.00 0.00 0.00
Andal ROO 51 Ramprasadpur 830 1.5 1.6 2.4 780 50 94 6.02 85 14.93 1.95 0.24
Jamuria OM1 28 Nigha 739 1.3 1.6 5.5 738 1 100 0.14 98 2.25 0.04 0.04
Andal ROO 50 Baska 780 1.4 1.6 3.2 690 90 88 11.54 56 44.41 3.51 0.69
Jamuria OM1 19 Nandi 1030 1.9 1.5 12.7 1030 0 100 0.00 100 0.00 0.00 0.00
Andal ROO 55 Dubchurieria 807 1.5 1.5 3.7 807 0 100 0.00 100 0.00 0.00 0.00
Jamuria ROO 53 Tapsi 962 1.7 1.4 8.4 962 0 100 0.00 100 0.00 0.00 0.00
a It has been divided into 4 parts
I:PS-Mouza = Information regarding Mouza,JL-No,respective PS,
II:Share to Total= Contribution of the particular towards total number of plots registered and total revenue,
III:Distribution:Land-Apt= Contribution of land and apartment towards plots registered and revenue of this mouza
IV:Apt-Anls= Contribution of Apartments towards Total number of registration and revenue
b PS= PS-Name,Mcd = Mouza-code geneally JL No,Mty= Mouza-Type,,Mouza= Mouza-Name;
c npts= Total number plots registered in this mouza,Pr-pts= Proportionate number to total,Pr_rev=Proportionate revenue,
avAR = Average area of land transferred in decimal,
nldpts= Number of land plots registered, nappts= that of apartment,
nld_pr = Land plot proportion to this mouza,nap_pr = Apartment plot proportion,
rld_pr = Revenue proportion of land to this mouza,
rap_pr = Apartment Revenue proportion;
d Tnap_pr = Apartment number contribution of this mouza to Total apartment Registration,
Trap_pr = Apartment revenue contribution of this mouza to Total Registration.

In Fig 2.14 top 20 revenue contributing mouzas along with their proportionate share of revenue and that of numbers involved in transaction have been plotted.

Important Mouzas: ADSR Raniganj

Figure 2.14: Important Mouzas: ADSR Raniganj

From Table 2.12 & Fig 2.14 above it is found that:

  • These 20 mouzas are mostly situated around Raniganj town, Andal area and Jamuria town. Also NH2 which runs across this jurisdiction has an important part in development of this area.
  • 3 mouzas namely, Raniganj Municipality (14.76%), Searsole(7.77%) and Amrasata(6.21%) contributes most towards the revenue of ADSR Raniganj.
  • All of these are ‘Municipality’ mouzas.
  • Together they contribute about 12% of total number of transferred plots,
  • Almost 50% of their revenue comes from apartment registration;
  • Together they contribute almost 87% of the revenue coming from apartment registration;
  • Apart from them, Bhadur of Andal PS is another mouza whose almost 50% of revenue comes from apartment registration;
  • Ramprasadpur(2.38 dec), Raniganj Municipality(2.66 dec),Baska(3.24 dec),Andal(3.58 dec), Bhadur(3.66 dec), Dubchururia(3.70 dec),Seasole(4.26 dec),(excepting Raniganj Municipality and Searsole all fall in Andal PS) having low average area transfer.
  • From Mouza-code it appaears all thesse Andal PS mouzas are more or less adjacent .It is supported by the Google imagery which says that they are situated in and around Andal Railstation and Andal ‘More’ (on NH-2) area which are well-marked urbanised area.28
  • Of these top mouzas, mouzas of Jamuria and Pandabeswar PS tend to transfer more area of land per transaction.

Of these mouzas Raniganj Municipality, Searsole and Amrasata have been taken up for further study of spatial distribution of market-value across the plot numbers and Fig 2.15 has been prepared. In it each dot represent a plot-number involved in transaction. Size of the dot varies with change in market-value - with size growing bigger for larger market-value. Plots involving land and apartment have also been coloured differently where transaction of land is represented by the blueish colour and that of apartment by the reddish one.

Market Value Distribution of Selected Mouzas  across Transferred Plots

Figure 2.15: Market Value Distribution of Selected Mouzas across Transferred Plots

It is found that

  • Registration generally tends to be concentrated in few pocekts.
  • For Raniganj Municipality these pockets lay around the plot-range of
    • 1-250,400-600,800-900,around 1200;
    • sparsely around 1400,1600;
    • around 2500-2600,2700-2900;
    • sparsely around 5000-5100; -around 5700,6600 etc.
    • more higher market-values are seen near range 1-250, around 1900 and around 2900, which sometimes exceeded Rs 1Cr;
    • apartment registration concentrated around plot ranges 1-250,2700-2900,around 5100,and spasely around 3700-4200;
  • For Searsole
    • concentration is witnessed around 1-100,around 400,around 1100-1200, around 2100-2600 and 4700-4900;
    • apartment registration is seen around 2100,around 2300,around 2500 and around 2800 and sparsely around 4800;
    • high values are seen near plots 100,700,4900;
  • For Amrasata number of plots are less than the previous two mouzas;
    • concentration however is seen in the plot-range 1-300,around 500, sparsely in 1000-1700, around 2400,around 2900, more concentrated around 3000-3100.
    • apartment registration is seen around 300,1350,1400,2900,3000-3050;
    • high-valued transaction happened around 100,500,1200 and 3000.

2.3.2.2 Analysis of Proposed Land use practice:

Registration data of ADSR Raniganj has been analysed to get insight of the use of proposed land uses during registration. Also comparative analysis with adjecent offices of ADSR Asansol and ADSR Durgapur has been taken up.

Primarily we can start with the number of land groups present in these offices.(For detailed discussion please refer to 2.3.1.2). Table 2.13 shows the landgroups of the offices of Paschim Burdwan. Primarily, it is seen that ADSR Raniganj has much more larger landgroups(34) than other offices.

Table 2.13: Number of landgroups: Offices of Paschim Buradwan
District_code Disrict ro_code office num_of_landgrps
23 Paschim Bardhaman 04 A.D.S.R. RANIGANJ 34
23 Paschim Bardhaman 06 A.D.S.R. DURGAPUR 15
23 Paschim Bardhaman 24 A.D.S.R. KULTI 15
23 Paschim Bardhaman 05 A.D.S.R. ASANSOL 8

Then proposed landuses used for ADSR Raniganj and ADSR Asansol have been analysed. Fig 2.16 depict a comparative analysis of proposed landuse used in transactions for rural mouzas of ADSR Raniganj and ADSR Asansol. In it heights of the columns represent the proportion of use in transaction, each colour represent a different use and numbers besides the columns represent the respective conversion ratios.

Comparative Analysis of Proposed Uses for Transfer: Rural Mouzas: ADSR Raniganj

Figure 2.16: Comparative Analysis of Proposed Uses for Transfer: Rural Mouzas: ADSR Raniganj

Fig 2.16 shows that-

  • Much more landuses were used in ADSR Raniganj compared to ADSR Asansol- as seen from the colour codes.
  • Main landuses for both these offices revolve around different types of agrilands like Baid, residential like Bastu,Vastu etc, Industrial uses like Land for industrial use etc.
  • Commercial types for both the offices have not been used much. It is expected as generally commercial transfers remain less in number compared to agri or residential purposes.
  • ADSR Raniganj shows use of different identical or semi-identical landuses which can be easily clubbed and the number can be brought down as ADSR Asansol.
  • large number of variation in the usages for ADSR Raniganj clearly shows that proposed land uses are relied heavily upon to reach the market-value of property.

Similar analysis has been undertaken for a municipality mouza each for ADSR Raniganj,ADSR Asansol and ADSR Durgapur. For ADSR Raniganj,mouza Raniganj Municipality; for ADSR Asansol, mouza Asansol Municipality; and for ADSR Durgapur, mouza Viringi have been chosen respectively. In Table2.14 a comparative analysis has been made by sorting out the conversion ratios used during land transfer of these mouzas. Also in it, number of uses belong to these ratios have been summarised. In Fig 2.17, these data has been plotted.

Number of Conversion ratio Groups used
Mouza:Asansol Municiaplity
Mouza:Raniganj Municipality
Mouza:Virigi
Table 2.14: Number of groups for Municipalities: Paschim Burdwan offices
ratio Number_of_uses
8 6
12 2
16 9
24 4
32 3
ratio Number_of_uses
2.5 2
4.0 1
4.5 1
6.0 1
8.0 1
10.0 4
12.0 3
13.0 2
20.0 2
35.0 4
ratio num
2.0 1
2.5 1
3.0 3
6.0 1
7.5 2
12.0 1

It is seen that

  • In ADSR Raniganj much more number of ratios (groups) have been used;
  • The number of ratios used for mouza Raniganj Municipality of ADSR Raniganj is 10;
  • The number of ratios used for mouza Asansol Municipality of ADSR Asansol is 5;
  • The number of ratios used for mouza Viringi of ADSR Raniganj is 6;
  • Expectedly, for Raniganj Municipality much more number of groups exist. For example- 4 groups exist between ratios 4 to 8.
  • Hence, for ADSR Raniganj market value determination is much more reliant on the use of conversion ratios- which may lead to more subjectivity.
Conversion Ratios used for Municipality mouzas: Paschim Burdwan

Figure 2.17: Conversion Ratios used for Municipality mouzas: Paschim Burdwan

In Table2.15 all the conversion ratios used for land transfers in these mouzas, their proportionate number, proportionate revenue coming from these ratios have been summarised.

Mouza:Asansol Municipality:Asansol
Mouza: Raniganj Municipality:Raniganj
Mouza: Viringi:Durgapur
Table 2.15: Comparative analysis of Proposed land uses used in Paschim Burdwan
use ratio prpnum prprv
Bastu 16 55.78 54.08
Vastu 16 31.55 23.94
Other Commercial Usage 32 2.24 8.14
Semi Commercial Usages 24 2.06 7.39
Shop 32 1.40 1.73
Pukur 8 1.18 1.48
Path 16 3.33 0.87
Nursery 12 0.06 0.75
Dokan 32 0.37 0.62
Godown 24 0.28 0.42
Pukur Parh 16 0.65 0.20
Viti 16 0.16 0.12
Car Parking Space 16 0.03 0.08
Rasta 16 0.31 0.07
Baid 8 0.06 0.03
PUKUR 8 0.06 0.03
Danga 12 0.09 0.02
Karkhana/Factory 24 0.03 0.02
Doba 8 0.06 0.01
Bhiti 16 0.03 0.00
Drain 8 0.09 0.00
Gudam 24 0.06 0.00
Mandir 16 0.03 0.00
Nala/Dren 8 0.06 0.00
use ratio prpnum prprv
Bastu 12.0 72.00 66.58
Vastu 12.0 12.16 11.76
Commercial Use 35.0 3.98 6.47
Other Commercial Usage 20.0 2.01 5.28
Pukur 4.0 4.43 2.82
Bagan 4.5 0.94 2.17
Danga 2.5 1.21 0.96
Khamar 6.0 0.72 0.80
Patit 10.0 0.49 0.74
Path 13.0 0.36 0.60
Dokan 35.0 0.13 0.47
Godown 20.0 0.45 0.44
Shop 35.0 0.09 0.33
Haspatal 10.0 0.04 0.30
Doba 8.0 0.09 0.13
School 10.0 0.04 0.11
Petrol Pump 35.0 0.09 0.01
Rasta 13.0 0.54 0.01
Drain 12.0 0.04 0.00
Mandir 10.0 0.04 0.00
Nala/Dren 2.5 0.13 0.00
use ratio prpnum prprv
Bastu 3.0 47.67 51.81
Vastu 3.0 42.90 36.21
Other Commercial Usage 12.0 7.88 9.28
Factory/Karkhana 6.0 0.39 1.02
Semi Commercial 7.5 0.49 0.53
Land for Industrial use 2.5 0.19 0.52
Garej 7.5 0.10 0.30
Maszid 3.0 0.19 0.24
Pukur(Machhchash 2.0 0.19 0.08
1 use = Proposed Land Use name used during Transaction;
2 ratio= Respective conversion Ratio;
3 prpnum = Proportionate number of this particular use for this mouza’s land transaction;
4 prprv = Proportionate revenue coming from this use for this mouza

From Table2.15, it is seen that:

  • for all these offices in municipality mouzas dominant proposed land uses are variants of ‘Residential uses’ like ‘Bastu’,‘Vastu’;
  • Together these two contribute over 80% of total land transfers and about 75% of revenue from the respective mouza’s land transfers;
  • Other dominant uses are ‘Commercial’,‘Semi commercial’ usages,together these two contribute about 10-15%.;29
  • For Raniganj Municipality mouza, uages like _‘Path(ratio=13)’,‘Patit( ratio= 10)’_have been found to be used.
  • These usgaes are around ‘Bastu’/‘Vaastu’(ratio=12), and can easily be clubbed with ‘Bastu’.
  • ’Patit’seems to have been used for less-developed plots near residential areas.
  • Rather than this, the concerned plots can be identified properly and their respective basevalues can be updated accordingly;

2.3.2.3 Assessment of Registration pattern:

Registration data of select offices can be taken up to have insights over the distribution of different transaction types, seasonal distribution of transactions, distribution of registration types.

2.3.2.3.1 Transaction types:
Table 2.16: Different Transactions and their Contribution:ADSR Raniganj
Transactions
Contribution
Transaction_Name Transaction_code number pernum per_rv
[0101] Sale, Sale Document 0101 41326 74.52 91.22
[0201] Gift, Gift in Favour of family members 0201 10270 18.52 3.93
[0139] Sale, Development Power of Attorney 0139 1709 3.08 0.28
[0207] Gift, Gift in f/o family members and others 0207 646 1.16 0.61
[0204] Gift, Gift in f/o others except family members, Government, Local Body 0204 355 0.64 0.72
[0143] Sale, Sale agreement without possession 0143 300 0.54 0.59
[0105] Sale, Sale after registered sale agreement without possession 0105 226 0.41 0.96
[0501] Partition, Partition 0501 217 0.39 0.32
[0403] Lease, Lease 0403 155 0.28 0.71
[0601] Exchange, Exchange 0601 94 0.17 0.19
[0109] Sale, Sale in favour of Government 0109 55 0.10 0.00
[0104] Sale, Sale agreement without possession [Full Stamp] 0104 32 0.06 0.31
[0408] Lease, Surrender of Lease 0408 18 0.03 0.00
[0103] Sale, Sale after registered sale agreement with possession 0103 15 0.03 0.00
[0110] Sale, Development Agreement or Construction agreement 0110 14 0.03 0.04
1 number = Number of plots involved in the respective transactions;
2 tot_rv = Total revenue from this particular transaction;
3 pernum = Proportion to total number of transactions;
4 per_rv = Proportion of revenue to tal revenue
Different Transactions:ADSR Raniganj

Figure 2.18: Different Transactions:ADSR Raniganj

These show “[0101] Sale, Sale Document” and “[0201] Gift, Gift in Favour of family members” are two most dominant forms of registration in terms of number (74.5% and 18.5% respectively), as well as revenue(91.2% and 3.93% respectively).30

2.3.2.3.2 Seasonal and time distribution of transactions:

Registration data of the select offices can taken up for exploring the distribution of transactions across months or weekdays or even across office- hours. These may help us -

  • identifying the existence of any unique local pattern prevalent in the offices apart from corroborating the general understanding that registration peaks generally around months of July-August, November-December, February- March which largely coincide with Bengali months of ‘Shravan’,‘Agrahayan’,‘Magh’,‘Falgun’ respectively;
  • exploring the existence of any pattern in these offices on to which days more registration occurs;
  • to explore the hourly pattern of registration and thereby to assess the pattern of footfall in a office during the office- hour.

For this analysis registration data of all the offices of Paschim Burdwan district, namely ADSR Asansol, ADsR Durgapur,ADSR Raniganj and ADSR Kulti have been taken up and analysed in comaprison to each other accordingly.

A.Month wise Distribution:

Here registration data of these offices have been analysed month wise and Figure 2.19 has been created by placing data of all offices in one.

Registration Across Months: Paschim Burdwan

Figure 2.19: Registration Across Months: Paschim Burdwan

Figure 2.19 shows the distribution of the registration across months of the offices of the Paschim Burdwan district.

  • It shows that during months December, July, August, February,November generally more footfalls occur in ADSR Raniganj and ADSR Durgapur.31.
  • However, September tops the list for ADSR Asansol which is not the case for others.

B.Weekday wise Distribution

Registration Across Weekdays:Paschim Burdwan

Figure 2.20: Registration Across Weekdays:Paschim Burdwan

Figure 2.20 shows the registration across weekdays in the offices of the Paschim Burdwan district.

  • It shows that generally on Fridays more number of registration occur.
  • However, for ADSR Kulti Wednesdays are more important in this respect than other days.
  • In ADSR Raniganj on Thursdays slightly less registration occurs with comparison to others, however in other offices- specially in ADSR Asansol and in ADSR Durgapur, there is not much difference in number of registration on Thursdays with other days immediately preceeding.

C:Hour of Registration

Hourly distribution of registration is a key indicator of the office activity related to public interaction. Generally, it can be assumed that time of presentation of a document precedes its time of registration by about 45 minutes to one hour. Hence hour of registration of documents in a office indicate the hour of presentation also. Moreover, number of transactions remaining high over longer intervals might indicate the sustained congregation of registrant public over a longer period of time in the office.In Figure 2.21 registration across office hours in the offices of the Paschim Burdwan has been plotted.

Registration across office hours

Figure 2.21: Registration across office hours

From Fig 2.21 it can be discerned that

  • Number of registration gets to peak during 1 pm to 2 pm for ADSR Asansol and ADSR Durgapur. It means that presentation of deeds tops in these offices between 12:30 pm to 1:30 pm. It indicates to following of more organised practices related to registration in these offices and most footfall occur genrally around 1-2 pm.

  • For ADSR Raniganj number of registration gets high values during 1-4 pm -with 2-3 pm gets marginally higher number- which means more congregation of people in the office during 1-3 pm.Keeping in view of the fact that both ADSR Asansol and ADSR Durgapur register much more documents annually than ADSR Raniganj, it indicates to following of lesser organised practices related to registration compared to ADSR Asansol and ADSR Durgapur.

  • Interestingly,for ADSR Kulti number of registration gets to peak during 11-12 pm. In this regard it may be mentioned that ADSR KUlti has been created by carving out the jurisdiction of erstwhile ADSR Asansol and persons related to registration are mainly based on Asansol - 15 km away from Kulti. It is possible then that people from Asansol coming with all prepared deeds, present it early at ADSR Kulti and return to Asansol as early as possible for attending other office works. Opening of DSR Paschim Burdwan then only for this reason may dent the registration at ADSR Kulti considerably and this aspect may be explored with later data.

2.3.2.4 Combining the Registration data with othet available data :

Registration data as discussed above can be analysed interacively with different sorts of data available on different platforms. These data can be ranged from available GIS data to ROR data, census data, road information if available from PWD, GST data with vendor information , data from agriculture department showing cropping intensity etc. These can render insights to know the nature of an area and to assess logically its potential as residential, agricultural, commercial or industrial area.

I.GIS data-

DoRSR has set up its own GIS project.

  • In it Shape files, containing the basic plot information-such as plot-number,area of all mouzas as available from L& LR department, Govt. of West Bengal, are being georeferenced at HQ level32.
  • Also shape files of metal rd,SH, NH as on 2014, have been made available to DoRSR and those have been put in one separate layer alon with the georefernced plot level shape files.
  • The registration data as discussed earlier, ROR information data have been put into the attributes of the GIS data received and made ready for on-the-click view (See Figure 2.22).
  • Also using GIS tools, areal distances of all plots from selected fetatures33 can be calculated.
  • Some basic analytics features are available in the GIS platform which can be helpful in visualising the output over maps;
  • Also all the data put into GIS attributes can be exported in excel or csv format which can be taken up for further analysis;
  • Also R or Python scripts used in these analysis can be put in the GIS directly for visualising the outputs over maps.
GIS mapping at DoRSR

Figure 2.22: GIS mapping at DoRSR

II. ROR data

ROR data acquired from L&LR department can be analysed simultaneously with registration data. Even GIS can render useful information like distance from selected features like ‘Bastu’ plot,‘Metal Road’ etc. Land classes can then be grouped and summarised over these distances to get the picture of general location of these classes relative to these features. Table 2.17 is an example of such grouping. It lists 20 classes- sorted alphabetically, under the jurisdiction of ADSR Durgapur.

Table 2.17: Different land classes under ADSR Durgapur and distances from Bastu, Metal_Rd:Example
Land Class Number Mid_Bastu Mid_Mtl_Rd
Bagaan 524 54 174
Baid 56817 255 385
Bakery 1 37 722
Baluchar 137 59 165
Bandh 45 197 149
Bans Bagaan 3 0 273
Bastu 15503 0 44
Bazaar 24 8 10
Beel 1 30 329
Bhagar 1 71 6
Bhiti 1229 0 58
Bohal 2441 238 531
Chanak Khad 2 124 174
Chankhola 10 0 398
Chaul Kal 1 87 81
Club 4 6 137
College 4 0 64
Dairy 1 45 12
Danga 6825 22 104
Debsthan 355 0 56
1 Land Class= Land Class as in ROR
2 Number = Respective number of each land class
3 Mid_Bastu = Median distance from nearest Bastu plots
4 Mid_Mtl_Rd = Median distance from neasest Metal Road

As these classes are put into groups and thereby conversion ratios by ADSRs - this analysis can be helpful be for preparation of proposal on conversion ratios by the ADSRs.

Fig 2.23 has been prepared by plotting the different land classes which are located within 100 mtrs (median distance) of ‘Bastu’ class.Each class is shown by a separate point and their sizes vary with the change in their respective number of occurrence in ROR data.

Land classes within 100 mtrs of Bastu Plots:ADSR Durgapur

Figure 2.23: Land classes within 100 mtrs of Bastu Plots:ADSR Durgapur

III.Census Data

Census data can be analysed with respect to the population of a particular area and can be explored whether number of transactions or market-value of immovable properties have any bearing with it. To check primarily, census data (2011) of areas under ADSR Raniganj have been taken up and in Fig 2.24 most populous areas with their respective population have been plotted.34

Populous areas under ADSR Raniganj

Figure 2.24: Populous areas under ADSR Raniganj

It is found that roughly, 60% of the places picked in Fig 2.24 coincide with the mouzas with most registrations under the jurisdiction of ADSR Raniganj. (Please refer to Fig 2.14). Hence there is ample scope of exploring this data along side,in analysis of market-value for ‘Scientific Framework’.

Similarly, GST data with details of dealers, the data from Agriculture department showing the cropping intensity, the data from PWD deapartment with road-details can be helpful in this regard.

Objective would be to explore wheather there are any relationship with market value and with them;

2.3.2.5 Building MV Model:

Generally for market- value determination of a property or preparation of MVDB ROs keep in mind a number of implicit or explicit factors which may be responsible for the market value of property. Although, ROs employ their field experience and application of mind- even the best practices may be very subjective. However, these factors or variables are very much quantifiable . They can be either continuous - i.e, measurable on any continuous scale like distance; or, categorical - i.e few qualities that can be grouped or ordered into some measurable quantities; or, binary - i.e, when presence of absence of any particular quality can be described by 1 or 0. Then collecting the data of market-value of property in a mixed way -

  • by independent means from field with the help of survey;
  • and by picking up the registration data where data-quality is better;

and using the factors responsible for market-value, with the help of statistical analytics tools, ’Market Value Model’s can be built.

However, if the chosen parameters can be reconciled with the outcomes of fileld survey and a diverse and vast amount of market-value data is collected from field the model can be more reflective of ground reality. Hence, coming on board of Indian Staistical Institute - Kolkata,as decided earlier, for conducting representative sample survey and building such model/models for representative areas, as well as training the DoRSR officers to conduct such for themselves in future would be very sought after.

Mouzas grouped in different plots transfer range:ADSR Durgapur

Figure 2.25: Mouzas grouped in different plots transfer range:ADSR Durgapur

Nevertheless, an attempt has been made to build such a model for a few select areas of ADSR Durgapur in the following way: (Please see Fig 2.26 which outlines the process.)

  • First a part of ADSR registration data has been chosen;
  • Distance from nearest bastu plot, nearest metal road have been measured for each plot;
  • Then each mouza has been categorised as per their yearly registration numbers (See Fig 2.25);
  • Then the data was parted into two parts- namely, trainset and testset;
  • For trainset regression has been done with known market-value of this set and set of parametrs to build the model;
  • Regression has been done to reach at ‘Market Value’ with different sets of parameters
    • One set with parameters -

      • Distance from Bastu,
      • Distance from metal road,
      • area of land,,
      • ROR classification,
      • Mouza-type and
      • Plots-transfer range;
    • another set with parameters-

      • Distance from Bastu,
      • Distance from metal road,
      • area of land,,
      • Mouza-type and
      • Plots-transfer range
    • For both these sets multiple regression methods were followed.35

    • It is found that the model with second set of parameters stated above yielded the best Rsquared36 value i.e without ROR classification37.

    • Then this model was employed to predict the market-value of the testset.

    • It has been found that parameters area of land,distance from metal road,mouza-type(Municipality1), plots-transfer-range(above 1K), distance from bastu , mouza_type(Rural) - ordered in descending order of importances- play most important determination of market-value in the best model.

    • Nevertheless, it should be kept in mind that, as these models have been built by training the existing the market value of the train-set which is the market-value generated by the ‘e-nathikaran’ system, the underlying weaknesses inherent in the system might creep in these models. Hence the market-value data of the train-set should be a mixture of both the data from field survey, as well as good market value data, which can be a near reflection of proper market-value on ground, identified from the system.

Regression process followed for model-building

Figure 2.26: Regression process followed for model-building

Hence analytics can play a very crucial role for DoRSR. It can be employed effectively for regular analysis of revenue data to visualize the trends in different zones of the state, to unearth the underlying pattern, similarities or dissimilarities at the revenue front across offices beyond district-limits and to lay stress on the typically untouched segments for revenue generation as well as for informed decision making- as it will help DoRSR looking beyond the numbers of revenue data only.

As analytics can be used for much deeper analysis of the office wise registration data, it can be helpful in detection of anomaly in mouza groups, conversion ratios, base values in effective way. It can render quality service to the DoRSR by helping the ROs in identifying the potential growth centers, by making them aware of the pattern of registration, spatial and seasonal distribution of market-value of properties across different mouzas or across different plot-numbers of selected mouzas under their respective jurisdictions. It can even be proved useful in rendering insights on the registration practices prevalent in an office, about the time-window of the congregation of registrant public at office and thereby on the possible pattern of its public-dealing.

Analytics can be helpful in exploring a range of GIS information, various data available on different government or other platforms in respect of market value of properties and can be effectively utilized in building market value models by making use of these data and any data that might come out of field survey. Hence it is recommended to start working of a full-fledged regular analytics wing at the headquarter level of DoRSR.


  1. Please refer to “RBI report on State Finances:A Study of Budgets for West Bengal(5th Table from top)” and corresponding CAG report on State Accounts for West Bengal Budget Estimates.↩︎

  2. Market value data base or MVDB - prepared and updated at regular interval by the officers of DoRSR ↩︎

  3. Market value is generated mainly by the combination of base value of the property and the multiplicative factor called ‘conversion ratio’ applied by the field -level officers. ↩︎

  4. This work is done at the district level by the District Registrars↩︎

  5. Statistical analytical tools like R, Python may be proved very handy in this respect. Initiatives have been taken up earlier for the enthusiastic officers to be trained in these languages through online platforms.↩︎

  6. for example, ROR data from L& LR Department, GST data specifying the details of dealers and respective returns, Data from Agriculture department, Census data, Road data of PWD department ↩︎

  7. Scientific Framework of Market Value has been envisaged with the aim to reduce the subjectivity in generation of market value of property.↩︎

  8. Market Value Data Base ↩︎

  9. basically smallest administrative units↩︎

  10. ROO in Conv_rate_Rural,DOO in Conv_rate_Developing,(RM1,OM1,DM1 in Conv_Rate_Municipality1,(RM2,OM2,DM2) in Conv_Rate_Municipality2,((RM3,OM3,DM3) in Conv_Rate_Municipality3) etc.Please see Table 2.7 for reference.↩︎

  11. DoRSR has planned to conduct survey for market value of properties in representative areas by organisation like, Indian Statistical Institute, Kolkata↩︎

  12. 65 during 2018-19, with 27 in 8000 to 10000, 22 in 10000 to 120000 and 27 in 12000+ category respectively↩︎

  13. 25 in 2018-19 and 2020-21, and 2019-20↩︎

  14. Please Refer to :“Census of India 2011: Primary abstract Data” also ref to:Spatial Perspectives of the New Census Towns, 2011: A Case Study of West Bengal by Guin and Das.2015. (Pg 117-122): These studies attribute to strong impact of Kolkata and the extension of small towns beyond their administrative boundaries for the considerable growth of new census towns or urbanised centres. For district wise number and types of census towns please refer to:Emergence of Census Towns and its Socio-Economic Condition: Case of West Bengal: Karmakar (Pg-25, Table 3)↩︎

  15. Please refer to Spatial Perspectives of the New Census Towns, 2011: A Case Study of West Bengal by Guin and Das.2015.(Pg 122)↩︎

  16. showing districtwise revenue collected and number of documents they registered in last three finacial years, growth in revenue and growth in doument numbers, their average revenue contribution in% and average documents in% they contribute to the state total↩︎

  17. It has two graphs: districtwise average revenue share, and districtwise average document share;↩︎

  18. It also has two graphs:Revenue growth of the districts in 2019-20;Revenue growth of the districts in 2020-21. In both, corresponding year’s state average has been shown as horizontal blue lines.↩︎

  19. Earlier, ARA-I had its jurisdiction over South 24 Parganas, Howrah, and ARA-II over North 24 Parganas.↩︎

  20. Uttarpara, Belgharia, Barrackpore falls within this category- however, not taken up for this particular discussion↩︎

  21. Flight of document to a particular office also depends on the Public Perception of that office - which is built mainly upon the effectiveness of service it renders↩︎

  22. Similar inferences can be drawn for district ADSR offices and corresponding DSR offices if more offices are worked upon.↩︎

  23. Also, property- value of these areas remain under constant scanner of different authorities and organisations; and even any attempt to rationalization- leave alone thorough overhaul- might not be very smooth.↩︎

  24. For exmple, transaction data of Ketugram has at least 20 mouzas under Developing category for which average market value does not exceed Rs. 7000 with total number of plots registered not exceeded 150 over a span of 3 financial years (2016-17,2017-18,2018-19- (upto January))↩︎

  25. ADSRs may propose change in the ratios during their annual updation proposal↩︎

  26. In statistics and probability theory, the median is the value separating the higher half from the lower half of a data sample, a population, or a probability distribution. For a data set, it may be thought of as “the middle” value. The basic feature of the median in describing data compared to the mean (often simply described as the “average”) is that it is not skewed by a small proportion of extremely large or small values, and therefore provides a better representation of a “typical” value. source;wikipedia↩︎

  27. Average are of land transfer is generally lower for more urbanised mouzas- areas. As more urbanised the area is, more would be the demand on land and less the availability.↩︎

  28. Hence average area of transferred land for an area can be an important indicative factor for identifying an urbanised area.↩︎

  29. ‘other commercial usage’ for semi-commercial in case of ADSR Raniganj.↩︎

  30. And these numbers for rest of the offices of Paschim Burdwan District are as follows: ADSR Durgapur: In terms of number of registration(80.6%, 12.8% respectively), in terms of revenue (96.0% and 1.34% respectively). ADSR Asansol: In terms of number of registration(73.2%, 14.8% respectively), in terms of revenue(91.6% and 1.34% respectively); and slightly significant numbers for [0139] Sale, Development Power of Attorney (6.09% and 0.34% respectively). ADSR Kulti: In terms of number of registration(83.3%, 11.0% respectively), in terms of revenue(94.6% and 2.72% respectively);and slightly significant numbers for[0139] Sale, Development Power of Attorney (2.66% and 0.24% respectively).↩︎

  31. These by and large coincide with the Bengali crop cycles and auspicious months: ‘Magh,Falgun’: Mid Feb to March, ‘Ashar-Shravan: Mid June to Mid August’ with July tends to be the busiest in registration offices; ‘Agrahayan’: Mid November to mid December’↩︎

  32. Desktop georeferencing is being carried out at the HQ level in respect of available ArcGIS satellite imageries.↩︎

  33. as for Dakshin Dinajpur district, distance from nearest metal roads and markets of all plots have been calculated, while for Paschim Burdwan District distance from nearest metal road has been calculated.↩︎

  34. Here CTs are ‘Census Town’, based on some quality of population and workforce residing in an area it can be termed as a ‘Census Town’.↩︎

  35. Random Forest and KNN process for regression were employed for both the sets.↩︎

  36. R2 is a statistic that will give some information about the goodness of fit of a model.In regression, the R2 coefficient of determination is a statistical measure of how well the regression predictions approximate the real data points. An R2 of 1 indicates that the regression predictions perfectly fit the data. source↩︎

  37. Random Forest method resulted the best R2 result.↩︎